PRIMER – A Regression-Rule Learning System for Intervention Optimization

2016 ◽  
pp. 307-321
Author(s):  
Greg Harris ◽  
Anand Panangadan ◽  
Viktor K. Prasanna
Keyword(s):  
Rule Learning ◽  
Learning System

Learning rules for information extraction

2002 ◽  
Vol 8 (2-3) ◽  
pp. 167-191 ◽  
Author(s):  
J. TURMO ◽  
H. RODRIGUEZ
Keyword(s):  
Information Extraction ◽  
Rule Learning ◽  
Learning System ◽  
Training Set ◽  
Learning Rules ◽  
Textual Data ◽  
New Learning ◽  
Automatic Knowledge Acquisition ◽  
Huge Variety ◽  
The Way

The growing availability of textual sources has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Most IE systems use knowledge explicitly represented as sets of IE rules usually manually acquired. Recently, however, the acquisition of this knowledge has been faced by applying a huge variety of Machine Learning (ML) techniques. Within this framework, new problems arise in relation to the way of selecting and annotating positive examples, and sometimes negative ones, in supervised approaches, or the way of organizing unsupervised or semi-supervised approaches. This paper presents a new IE-rule learning system that deals with these training set problems and describes a set of experiments for testing this capability of the new learning approach.

Acceptance of Rules Generated by Machine Learning among Medical Experts

2001 ◽  
Vol 40 (05) ◽  
pp. 380-385 ◽  
Author(s):  
S. Mani ◽  
W. R. Shankle ◽  
M. J. Pazzani
Keyword(s):  
Machine Learning ◽  
Care Delivery ◽  
Medical Knowledge ◽  
Rule Learning ◽  
Learning System ◽  
Data Sets ◽  
Medical Databases ◽  
Monotonicity Constraints ◽  
Cost Of Health Care ◽  
The Cost

Summary Objectives: The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful. Methods: Two data sets, taken from problems as diverse as screening for dementia and assessing the risk of mental retardation, were collected and a rule learning system, with and without monotonicity constraints, was run on each. The rules were shown to experts, who were asked how willing they would be to use such rules in practice. The accuracy of the rules was also evaluated. Results: Rules learned with monotonicity constraints were at least as accurate as rules learned without such constraints. Experts were, on average, more willing to use the rules learned with the monotonicity constraints. Conclusions: The analysis of medical databases has the potential of improving patient outcomes and/or lowering the cost of health care delivery. Various techniques, from statistics, pattern recognition, machine learning, and neural networks, have been proposed to “mine” this data by uncovering patterns that may be used to guide decision making. This study suggests cognitive factors make learned models coherent and, therefore, credible to experts. One factor that influences the acceptance of learned models is consistency with existing medical knowledge.

Information Pre-Processing using Domain Meta-Ontology and Rule Learning System

2010 ◽  
pp. 207-217
Author(s):  
Girish R. Ranganathan ◽  
Yevgen Biletskiy
Keyword(s):  
Rule Learning ◽  
Learning System

Effects of pretraining and stimulus composition on rule learning.

1969 ◽  
Vol 80 (3, Pt.1) ◽  
pp. 450-454 ◽  
Author(s):  
Peter J. Johnson ◽  
Roger H. White
Keyword(s):  
Rule Learning

An Adaptive and Learning System for Feedback-Controlled Evoked Response Studies

1981 ◽  
Vol 20 (03) ◽  
pp. 169-173
Author(s):  
J. Wagner ◽  
G. Pfurtscheixer
Keyword(s):  
Brain Activity ◽  
Evoked Response ◽  
Learning System ◽  
Time Interval ◽  
Electrical Brain Activity ◽  
Long Time ◽  
The Subject ◽  
Eeg Power ◽  
Stimulation System ◽  
Background Eeg

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.

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